Leveraging clinical sleep data across multiple pediatric cohorts for insights into neurodevelopment: the Retrospective Analysis of Sleep in Pediatric (RASP) cohorts study

Abstract

Sleep disturbances are prominent across neurodevelopmental disorders (NDDs) and may reflect specific abnormalities in brain development and function. Overnight polysomnography (PSG) allows for detailed investigation of sleep architecture, offering a unique window into neurocircuit function. A better understanding of sleep in NDDs compared to typically developing children could therefore define mechanisms underlying abnormal development in NDDs and provide avenues for the development of therapeutic interventions to improve sleep quality and developmental outcomes. Here, we introduce and characterize a collection of 1527 pediatric overnight PSGs across five different sites. We first developed an automated stager trained on independent pediatric sleep data, which yielded better performance compared to a stager trained on adults. Using consistent staging across cohorts, we derived a panel of EEG micro-architectural features. This unbiased approach replicated broad trajectories previously described in typically developing sleep architecture. Further, we found sleep architecture disruptions in children with Downs Syndrome (DS) that were consistent across independent cohorts. Finally, we built and evaluated a model to predict age from sleep EEG metrics, which recapitulated our previous findings of younger predicted brain age in children with DS. Altogether, by creating a resource pooled from existing clinical data we expanded the available datasets and computational resources to study sleep in pediatric populations, specifically towards a better understanding of sleep in NDDs. This Retrospective Analysis of Sleep in Pediatric (RASP) cohorts dataset, including staging annotation derived from our automated stager, will be deposited at https://sleepdata.org. Statement of significanceWe introduce the RASP cohorts, a collection of 1527 clinical pediatric overnight polysomnographies that includes typically developing and neurodevelopmental disorder cases. As a first step towards addressing the analytic bottleneck inherent in manual sleep staging, we developed and validated a pediatric-specific sleep stager. Leveraging the retrospective RASP cohorts dataset, we redemonstrated known developmental trajectories in sleep architecture. To summarize changes in brain function reflected in sleep, we developed a model to predict brain age from sleep measures. We recapitulate younger predicted age in RASP Downs Syndrome cases. This work not only enhances our understanding of sleep disturbances in NDDs, but also provides a valuable resource for future research and underscores the utility of existing clinical polysomnography studies.